The endemicity of zoonotic trematode metacercariae (ZTM) was investigated with total 871 freshwater fishes (19 species) from Deokcheon-gang (a branch stream of Gyeongho-gang) in Sancheong-gun, Gyeongsangnam-do, Korea for 3 years (2018-2020). All fishes were examined with the artificial digestion method. The metacercariae of Clonorchis sinensis (CsMc) were detected in 233 (36.3%) out of 642 fish in 11 positive fish species (PFS), and their infection intensity was 27 per fish infected (PFI). Especially, in index fish, Puntungia herzi, of CsMc infection, prevalence was 64.2% and infection intensity was 37 PFI. Metagonimus spp. metacercariae (MsMc) were found in 760 (87.5%) out of 869 fish in 18 PFS and their infection intensity was 228 PFI. In sweet smelt, Plecoglossus altivelis, the prevalence of MsMc was 97.6% and their infection intensity was 3,570 PFI. Centrocestus armatus metacercariae were detected in 209 (29.4%) out of 710 fish in 8 PFS and their infection intensity was 1,361 PFI. Echinostoma spp. metacercariae were found in 293 (42.6%) out of 688 fish in 15 PFS and their infection intensity was 5 PFI. Metacercariae of Clinostomum complanatum and Metorchis orientalis were also detected in 2.7% and 21.2% fish in 4 PFS and their infection intensities were 3.1 and 3.4 PFI respectively. By the present study, it was confirmed that some species of ZTM including CsMc and MsMc are more or less prevalent in fishes from Deokcheon-gang in Sancheong-gun, Gyeongsangnam-do, Korea.

Objective: This study evaluated the clinical effectiveness of Minds.NAVI, a depression screening kit combining psychometric measures and stress hormone biomarkers, in a prospective clinical trial. The objective was to assess its potential as a depression screening tool and investigate the associations between psychological assessments, salivary hormone staging, and depression severity. Methods: Thirty-five participants with major depressive disorder and 12 healthy controls (HCs) were included. The Minds.NAVI software, utilizing the PROtective and Vulnerable factors battEry Test (PROVE) and salivary cortisol/dehydroepiandrosterone (DHEA) analysis, was employed. The PROVE test is a comprehensive self-report questionnaire that assesses depressive symptoms, suicide risk, attachment style, adverse childhood experiences, mentalization capacity, and resilience. In addition, salivary cortisol and DHEA levels were measured to evaluate the functional stage of the hypothalamic–pituitary–adrenal (HPA) axis. Results: Minds.NAVI exhibited 100% sensitivity, 91.7% specificity, and 97.9% accuracy in distinguishing depression from HCs within an exploratory small group. Salivary stress hormone phases showed changes with depression stage (p=0.030), and the proportion of patients with “adrenal exhaustion stage” was higher in the moderate/severe depression group (p=0.038). Protective/vulnerable factors differed significantly between controls and depressed groups (p<0.001). Cortisol awakening response inversely correlated with depressive symptom severity (r=-0.31, p=0.034). Conclusion This study suggested possible clinical effectiveness of Minds.NAVI, a depression screening tool that integrates psychometric measures and stress hormone biomarkers. The findings support the potential association between depression, chronic stress, and HPA axis hyporesponsiveness.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.

This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first hackathon of Genomics & Informatics Annotation Hackathon (GIAH) event. Extracting text from multi-column biomedical documents such as Genomics & Informatics is known to be notoriously difficult. The hackathon was piloted as part of a coding competition of the ELTEC College of Engineering at Ewha Womans University in order to enable researchers and students to create or annotate their own versions of the Genomics & Informatics corpus, to gain and create knowledge about corpus linguistics, and simultaneously to acquire tangible and transferable skills. The proposed projects during the hackathon harness an internal database containing different versions of the corpus and annotations.